code: def asarray(
      obj: Union[
          tf.Tensor,
          tf.Variable,
          tf.TensorShape,
          bool,
          int,
          float,
          NestedSequence,
          SupportsBufferProtocol,
          np.ndarray,
      ],
      /,
      *,
      copy: Optional[bool] = None,
      dtype: Optional[tf.DType] = None,
      device: Optional[str] = None,
      out: Optional[Union[tf.Tensor, tf.Variable]] = None,
  ) -> Union[tf.Tensor, tf.Variable]:
      # convert the input to a tensor using the appropriate function
      with tf.device(device):
          if tf.is_tensor(obj):
              ret = tf.cast(obj, dtype) if obj.dtype != dtype else obj
          elif dtype.is_integer and np.issubdtype(
              (obj_np := np.array(obj)).dtype, np.floating
          ):
              ret = tf.convert_to_tensor(obj_np, dtype)
          else:
              ret = tf.convert_to_tensor(obj, dtype)
          return (
              tf.identity(ret)
              if (copy or ret.device != device)
              else ret
          )
  
imports: import numpy
  from numbers import Number
  from typing import Union, List, Optional, Sequence, Tuple
  import tensorflow
  import ivy
  from ivy.func_wrapper import with_unsupported_dtypes
  from ivy.functional.ivy.creation import _asarray_to_native_arrays_and_back, _asarray_infer_device, _asarray_infer_dtype, _asarray_handle_nestable, NestedSequence, SupportsBufferProtocol, _asarray_inputs_to_native_shapes
  from .None import backend_version
label_desc: Use image node_id 2 for calling a global function with example usage: asarray() and returns: unknown